RELIABILITY ASSESSMENT IN SUGARCANE INDUSTRY USING … · 2016-11-11 · 2.6. Weibull probability...

RELIABILITY ASSESSMENT IN

SUGARCANE INDUSTRY USING

MONTE CARLO SIMULATION AND

THE METHODOLOGIES OF

RELIABILITY CENTERED

MAINTENANCE AND TOTAL

PRODUCTIVE MAINTENANCE

Celso Aurelio de Morais Lima (PUC )

[email protected]

Emerson de Souza Campos (PUC )

[email protected]

Maria Jose Pereira Dantas (PUC )

[email protected]

Ricardo Luiz Machado (PUC )

[email protected]

The purpose of this article is to develop a reliability evaluation about

the data gaps in a sugarcane production line. The report has a

quantitative approach with hybrid search method: combination of case

study and simulation. The methodoloogy of the study was supported in

Reliability Centered Maintenance (RCM) and Total Productive

Maintenance (TPM), resulting in a proposal for improvement of

maintenance strategies. Through ExpertFit ® software, the Time to

Next Failure (TTNF) of the system was modeled. Through Microsoft

Excel ®, a Monte Carlo simulation (MC) was held. The simulated

reliability was 56.27% for an hour of production.

Palavras-chave: Reliability, Monte Carlo simulation, Centered

Reliability Maintenance, Total Productive Maintenance.

XXXVI ENCONTRO NACIONAL DE ENGENHARIA DE PRODUCÃO Contribuições da Engenharia de Produção para Melhores Práticas de Gestão e Modernização do Brasil

João Pessoa/PB, Brasil, de 03 a 06 de outubro de 2016.


João_Pessoa/PB, Brasil, de 03 a 06 de outubro de 2016. .

2

1. Introduction

The 2nd World War influenced on many global changes envisioned in recent decades. On the

issue of technology, American researches on the arms industry were fundamental, along with

the development of industrial automation, both leveraged by the development of information

technology and telecommunication. In the social sphere, there is the dependence of the

contemporary society on the automatic modes of production on a large scale, with high

quality and low cost. The maintenance engineering has been vital in increasing search for

projects and economical and reliable operations for production systems. (ARAÚJO, 2011).

Pinjala et al. (2006) state that currently the industrial maintenance has become key in the

scenario of industrial businesses, since the maintenance strategy can positively or negatively

affect competitive aspects of manufacturing, such as cost and quality. According to Ramos

Filho, Atamanczuk & Marcal (2010), the importance of manufacturing is increasing in

business, requesting coordination and good strategy maintenance department, mainly due to

the growing demand for availability of machinery and equipment.

According to Fogliatto and Ribeiro (2009), as a tool to aid in the increasing of the reliability

of critical items, it is highlighted the area of knowledge approached by the Reliability

Centered Maintenance (RCM). Its applications have been recognized as effective ways to

increase the availability of equipment, minimizing costs related to defects, repairs and

replacements

Concurrently with the principles of RCM, the concepts Total Productive Maintenance (TPM)

are directed to raise the quality of industrial operations through a high efficiency of

maintenance management. According to Takahashi et al. (1993), TPM is one of the most

effective methods in the evolutionary process of transforming an industry in an operation with

oriented management to equipment.

As mentioned by Sellitto (2005), the reliability functions are dealing with random variables.

Gnedenko (1965) cited Sellitto (2005), explains that random variables do not contain fixed

values, i.e, they vary with occasional factors. The knowledge of a random variable is not

given to the determination of its numerical value, but for, the odds of this variable take each

value. Thus, the need for the application of stochastic methods to study reliability arises. The

Monte Carlo simulation (MC) is one of these methods. According to Martins, Wener & Pinto



3

(2010), the MC method used in the calculation of probabilities, there is good precision and

low complexity of computational modeling. For each random variable several random values

are generated, which represent the system dynamics.

The objective of this study was to develop a reliability study of a sugarcane production line,

responsible for reception, preparation and extraction of sugarcane. They collected data from

the system failure which resulted in downtime production. With that, the TPM and RCM

concepts were used in the improvement of maintenance strategies, with the preparation of a

maintenance plan. In addition, data for Time to Next Failure (TTNF) were modeled using the

Expertfit ® software, obtaining good results for the Weibull distribution probability. Finally,

the MC simulation model was developed in Microsoft Excel ® envisioning the reliability and

performance of the simulated system.

2. Literature review

2.1. Reliability centered maintenance

From the 60s, it was developed a detailed study for the definition of standards and procedures

for the maintenance of the aviation industry, based on extensive statistical analysis, known as

MSG-3 9 (Maintenance Steering Group), was the essence for what Nowlan & Heap (1978)

named Reliability Centered Maintenance (RCM). (RAPOSO, 2004).

According to Rausand (1998), after the application of the RCM in other industrial sectors,

experiments have shown significant reductions in preventive maintenance costs while

maintaining or improving the availability of their systems.

It is noticed in recent literature the tendency to proposals for improving models for the

traditional RCM methodology. Pexa et al. (2014), proposed a unified application of three

widely used tools: RCM, Safety Instrumented Function Process (SIFpro) and Risk Based

Inspection (RBI). Cheng et al. (2008) suggested a clever method for analysis of traditional

RCM. Selvik & Aven (2010) developed studies integrating uncertainty analysis in assessing

the RCM decision diagram, in which the simple answer procedure based on the traditional

Yes and No is insufficient.

Simply, Selvik & Aven (2010) argue that the RCM is a basic process that consists of two

stages: (i) intuitive analysis of potential failures, which are generally used in a critical Failure

Modes Effect Analysis (FMEA); (ii) application of the logic diagram decision to specify the



4

type of maintenance. In this study both steps (i and ii) were applied. Figure 1 shows the logic

diagram of decision.

Figure 1 - Reliability centered maintenance: logical diagram of decision

Source: Adapted from Lafraia (2014)

2.2. Failure mode and effect analysis - FMEA

FMEA is a reliability technique that basically has three objectives: (i) identify and analyze

potential failures inherent to a product / process; (ii) recognize actions that mitigate the risks

of such failures occur; (iii) prepare a document to be reference in the next review and

improvements. The FMEA techniques expose weaknesses in the system, offering subsidies

for continuous improvement of activities. Thus, it helps in detecting and eliminating potential

failures. (FOGLIATTO & RIBEIRO, 2009).

In this study, a basic implementation of FMEA was developed as proposed by Lafraia (2014)

in order to conduct the reliability analyzes.

2.3. Total productive maintenance - TPM

Currently the manufacturing facilities of various areas coexist with several types of waste.

Because these waste targets, oriented goals to zero case record, zero tolerance for defects,

breakdowns, accidents and waste - are becoming a prerequisite in factories. To overcome

challenges, the concept of Total Productive Maintenance (TPM) has been adapted in

industries worldwide. (SINGH et al., 2012).



5

TPM is a synergy among all business functions, particularly between production and

maintenance, focusing facing the continuous development of product quality, as well as to

ensure the capacity and operating efficiency. (CHAN et al., 2003).

2.4. Overall equipment efficiency - OEE

The Overall Equipment Effectiveness (OEE) is a key indicator for TPM. The OEE also covers

aspects of manufacturing. Not only evaluates the reliability and performance, but also the

efficiency related to losses due to rework and income loss. (ANVARI, EDWARDS &

STARR, 2010).

The OEE calculation is defined as the product of three variables: (i) Availability; (ii) speed

and (iii) rate quality. Availability checks the percentage of actual time spent in operation,

ranging between 0 and 1. Speed Rating analyzes the percentage of the relative speed (assumed

during deviation) over the nominal speed, assuming values between 0 and 1. Quality rate

checks the percentage output line of products, ranging between 0 and 1. The calculation is

shown in Equation 1. (FOGLIATTO & RIBEIRO, 2009).

2.5. Reliability and maintainability

According to Morad, Mohammad & Sattarvand (2014), reliability is the appropriate indicator

for quantitative evaluation in a survival analysis of any system. According to Meyer (1983),

the likelihood reliability is the probability of a product / component to develop its role within

the design conditions over a period of time. According to Diedrich & Sellitto (2014), the

reliability function R (t) is the probability that a device works without failures, ranging

between 0 and 1. Three of the variables used most in the reliability study are: MTBF (Mean

Time Between Failure), R (t) (Reliability) and h(t) (Risk Function). The MTBF calculation is

shown in Equation 2.

Maintainability is defined as the ability of a device to be repaired to restore its normal

operating functions after a failure. MTTR (Mean Time to Repair) is a good indicator of

maintainability. MTTR calculation is shown in Equation 3. Through the MTBF and MTTR, it

is calculated the Av (t) Availability as in Equation 4. (FOGLIATTO & RIBEIRO, 2009).



6

(2) (3) (4)

Where: T – Total time; N – Number of failures; TTNR – Time to Next Repair.

2.6. Weibull probability distribution

According to Meyer (1983) the Weibull distribution was originally proposed by W. Weibull

in 1954, when he conducted studies aimed to the failure time due to fatigue in metals.

According to Fogliatto and Ribeiro (2009), this distribution is appropriate for modeling stable

failure rates, increasing and decreasing ones, in the case of a significant distribution for

reliability modeling. Lafraia (2014) gives formulas for the Weibull distribution, the

distribution for fault f (t) function or risk failure rate λ (t) and reliability R (t):

(5) (6) (7)

Where: β - Parameter form; η - Parameter range; t - Variable time.

In this article, although other probability distributions were also adjusted, it was set to work

with the Weibull due to its flexibility, since it is possible to model all permissible states for

the failure rate λ(t).The simple analysis of the shape parameter allows you to see if the failure

rate (hazard function) is increasing, decreasing or is constant. Under this assumption, the

Weibull modeling becomes increasingly important for management and maintenance

engineering. Figure 2 shows the bathtub curve representing the possible states an equipment

life. It is observed that the value of the shape parameter β easily allows to assess the state in

which its equipment is.

Figure 2 – Bathtub Curve

Source: Adapted by Sellito (2005)



7

2.7. Monte Carlo simulation

The MC simulation is a statistical method used in stochastic simulations with several

applications. It includes modeling system in question and the production of random numbers

from the probability distributions, which represent the dynamics of the elements system and

the use of results to approximate the results. (MENDES, 2011).

The MC method for probability calculation is supported on random simulations. It shows easy

understanding, good accuracy level and low complexity to computational implementation,

and a lot demanded by engineers. The MC process of modeling and simulation involves the

following: (i) for each output variable, distribution; (ii) a susceptibility list of variable keys

organized according to their correlation with the output variable; (iii) graphics and statistical

reports characterizing the simulated responses. According to the type of problem, the

independent variables may use different probability distributions: Normal, Log-Normal,

Exponential, Triangular, Uniform and Weibull. (MARTINS, WENER & PINTO, 2010).

3. Case study: sugarcane production line

The reliability study was carried out in a sugar, ethanol and electricity industry, located in the

southwest of Goiás, Brazil. For the research, it was selected the production line 01, called by

the company, as reception, preparation and extraction - 01. This line accounted for

approximately 50% of the processing of the raw material (sugarcane), working continuously,

24 hours a day for about eight months (crop).

As it can be seen in Figure 3 flow chart, this case study will be considered, for simplification,

the production line will be divided into four subsystems: Reception and preparation, diffusion

extraction, milling 1 and 2 extractions. Any failure presented in one of the subsystems will be

equivalent to a production downtime.

Figure 3 - Production line flow chart 1

Source: the authors (2016)



8

Downtime data were recorded by the company through daily reports, subsequently entered

into the maintenance system, containing the type, reason and duration. Data were collected

from the production line fault 01 from 06.30.2014 (early harvest) to 12/03/2014 (last crop

fortnight), totaling 3768 hours of production (24 h / day of production). The downtime data,

used for charts preparation, are presented in Figure 4.

Figure 4 – Downtime data of subsystems

Time to Next

Failure (h)15,00 108,47 3,23 172,60 258,03 133,33 992,35 711,67 27,40 343,75 753,52

Time to Repair (h) 2,08 1,30 0,32 2,00 0,33 0,60 0,03 1,18 0,22 0,78 0,70

26,23 3,17 150,70 1,92 0,02 2,00 0,00 4,12 1,00 31,12 0,02

303,37 16,13 90,03 0,28 1,53 0,28 0,57 0,37 0,58 0,51 0,55

2,02 127,50 0,62 51,58 0,02 73,15 80,85 50,62 766,28 200,75 752,57

20,50 210,42 30,95 56,63 34,78 202,67 135,75

0,02 0,12 6,00 4,07 4,00 1,00 0,33 0,85 1,60 1,58 4,00

0,68 0,03 0,17 0,33 0,02 0,02 0,02 0,02 0,02 0,02 0,02

0,02 0,97 11,30 19,03 0,33 0,37 0,17 0,30 4,48 1,40 7,33

3,75 0,12 12,83 6,20 3,22 9,07 0,48

26,32 11,93 38,07 0,43 0,77 98,17 16,58 0,12 14,77 4,23 131,35

15,08 120,73 69,70 16,23 15,47 120,53 281,15 0,45 27,82 75,48 1515,87

272,90

1,33 0,08 0,17 0,50 0,75 0,23 0,55 0,23 2,00 0,53 0,05

0,33 0,03 0,45 0,15 0,67 0,20 0,07 0,30 0,05 0,17 1,97

0,25

18,03 0,00 0,02 0,18 6,68 0,00 0,00 32,98 12,58 9,57 84,52

0,53 0,88 0,70 0,17 0,85 6,75 0,15 34,57 25,15 81,93 4,43

0,60 4,78 9,75 5,30 2,97 16,88 8,05 7,32 0,63 1,53 0,05

0,00 57,87 4,97 0,00 53,47 42,73 1,07 4,52 155,33 3,97 1,20

7,38 30,55 43,08 43,50 7,08 53,22 8,43 22,52 29,23 0,78 5,03

18,27 20,60 20,25 8,10 2,77 23,32 11,20 32,48 8,97 0,00 34,68

12,37 479,73 7,25 421,38 93,83 73,55 177,75 84,98 594,10 16,97 16,65

52,15 67,97 0,08 35,88 28,92 253,68 66,90 35,08

4,63 1,32 0,92 0,68 1,70 1,00 2,00 0,25 0,10 0,05 0,08

0,50 0,70 0,07 0,15 1,00 0,22 0,08 0,55 0,37 0,08 0,13

0,08 0,10 0,05 0,07 0,12 0,05 0,08 0,08 0,08 0,15 3,00

0,38 0,30 0,48 2,00 0,07 0,12 0,12 0,20 0,17 0,05 0,67

0,07 0,75 0,17 0,05 0,15 0,08 0,05 0,03 0,03 0,03 0,27

0,07 0,05 0,17 0,08 0,03 0,68 0,12 0,05 0,53 0,33 0,07

0,10 2,97 3,83 0,12 0,12 1,25 0,27 0,42 1,22 0,88 0,03

0,08 0,07 0,07 0,10 0,05 0,12 0,08 0,08

Milling 01 Extraction

Time to Next

Failure (h)

Time to Repair (h)

Milling 02 Extraction

Time to Next

Failure (h)

Time to Repair (h)

Reception and Preparation

Diffusion Extraction

Time to Next

Failure (h)

Time to Repair (h)



9


3.1. Downtime data analysis

The failure data show that July was the most critical month related to the number of failures

and downtime, Figure 5 and Figure 6. Notice that the trend of failure decreases until

September, stabilizing it, and in November increasing again. This feature represents the

behavior system during the harvest, where there is greater amount of failures in the industry

start-up and at the end of the harvest, there is a moderate increase in failures.

About the studied subsystems, Figure 7, there is a high number of failures in Milling 2,

Figure 8. It shows that the Milling 2 has the lowest MTBF, resulting in a low reliability. As

for the downtime, Figure 8, the diffuser is a major cause of the downtime production line,

representing 106.27 hours, or 4.43 days. The diffuser can be considered t the most striking

bottleneck because, despite having fewer failures than Milling 2, it has longer downtime.

Figure 5 - Number of failures x months Figure 6 – Downtime (hours) x months

Figure 7 - Number of failures x subsystem Figure 8 – Downtime (hours) x subsystem


Through the failure data, MTBF, MTTR and availability values were calculated for each

subsystem using Equations 2, 3 and 4. The values are presented in Figure 9. As mentioned, it



10

was observed that the MTBF of Milling 2 presents the lowest rate. MTTR Diffuser has the

largest value, so its availability is the smallest among the subsystems. Multiplying the

availability of each subsystem, one obtains the overall 95.39% system availability.

Figure 9 - MTBF, MTTR and availability of subsystems


3.2. Overall equipment efficiency of subsystems

Following the proposal of this reliability study, in this section the OEE will be calculated. The

result is important to determine which subsystems efforts should be concentrated for

improvements and investments.

Firstly, the availability with MTBF and MTTR data were calculated, using Equation 3, and

they were presented in Figure 9. Then, through the number of failures for each subsystem,

extracted from the failure data, it was estimated speed ratio, given by the relation between the

time that the production system operated at reduced speed (due to a failure) and the time when

operated at normal speed. It is estimated for this production line for each downtime,

regardless of time, it takes on average 10 minutes to reestablish the nominal production speed,

i.e., is a downtime represents a 10 minute reduction in production speed. Thus, it was

calculated on the basis of the failure number, the total time that each subsystem worked at a

reduced speed. Considering the studied period studied - 3768 total hours (24 h / day of

production) – it was possible to find the speed rate through ratio reduced speed time and time

at normal speed. Figure 10 presents the calculation of the speed ratio.

Figure 10 - Speed rate calculation



11


The quality rate is calculated as deviation from the main rate quality of each subsystem index

relative to the expected rate (rate goal): preparation rate for the reception and preparation,

sucrose extraction ratio for the diffuser, bagasse moisture content, common to grinding mill

01 and 02. Figure 11 presents the calculations of quality rate. Finally, OEE is calculated using

Equation 1 presented in subsection 2.4, as presented in Figure 12.

Figure 11 - Quality rate calculation


Figure 12 - OEE Calculation


3.3. MCC: Failure mode and effect analysis (FMEA) and maintenance plan

In this section the goal is to develop an FMEA and the maintenance plan based on MCC

decision diagram. These tools have been applied only to the extraction through diffusion

subsystem, since it has the lowest OEE, Figure 12, and largest downtime, Figure 8. The

FMEA development was divided into two items. The first one, Figure 13, defines the failure



12

modes shown in the diffusion extraction line. The second one, Figure 14, is intended to

identify the effects of failures, allowing the establishment of a maintenance plan, Figure 15,

based on MCC decision diagram, Figure 1.

Figure 13 – (Part 1) FMEA: definition of failure modes to diffusion extraction

Item Function Functional Failure Fail Mode

a. Unable to transport the

sugarcane bagasse.

1. Electric motor with problem.

2. Mat drive with problem.

3. Reducer mechanical damaged.

4. Automation with problem.

5. Conveyor chain with problem.

b. Inappropriate leveling

sugarcane bagasse.

1. Flaps misfits.

2. Shovels drag with problem.

c. No leftover bagasse for

the return belt.

1. Bagasse input insufficient.

1. Distribute all diffuser

input sugarcane bagasse,

controlling the level.

Input Mat


Figure 13 – (Part 2) FMEA: definition of failure modes to diffusion extraction

Item Function Functional Failure Fail Mode

a. Unable to pull the gear

shaft.


2. Diffuser drive with problem

3. Reducer mechanical with problem.


5. Diffuser shaft with problem.

b. Unable to pull the

conveyor chains.

1. Traction gear with problem.

2. Conveyor chain with problem

Conveyor

chain3. Transporting the bagasse

mattress in unidirectional

sense during the diffusion

extraction cycle.

a. Unable to pull the

bagasse mattress.

1. Chain link with problem.

2. Conveyor chain with inappropriate

length.

3. Pins with problem


Output

Mat

4. Transporting the bagasse

diffuser to the next step of

the process.

a. Unable to transport the

bagasse.



3. Reducing mechanical damaged.


5. Mat of belt with problem.

2. Pull the conveyor chains

diffuser.

Diffuser

drive


Figure 14 – (Part 1) FMEA: definition of failure effects to diffusion extraction



13

Fuctional

FailureFail Mode Basic Failure Cause Effects of Failure

Loss of insulation by high humidity.

Breaking the bearing.

Electronic component with problem.

Disarm devices.

Inefficient lubrication.

Damage by fatigue.

Problem supervisory / logic

Problems with devices and instruments.

Conveyor chain locked.

Damage by fatigue.

1. Flaps misfits. Flaps misfits by fatigue.

Shovels drag locked.

Damage by fatigue.

FF 1.c 1.Bagasse input insufficient. Low speed of the production line. Unevenness of

bagasse.

Bushing water at the

exit of diffuser.

Loss of insulation from excessive

moisture



Disarm devices.


Damage by fatigue.



Damage by fatigue.

Falha de projeto.



5. Conveyor chain with problem.

FF 1.a

FF 2.a 1. Electric motor with problem.

2. Diffuser drive with problem.

3. Reducer mechanical with

problem.


Stop feeding the cane

on Diffuser.

Stop Diffuser.

Stop the production

line.


FF 1.b Unevenness of

bagasse.

Bushing water at the



Stop Diffuser.

Stop the production

line.

5. Diffuser shaft with problem.


Figure 14 – (Part 2) FMEA: definition of failure effects to diffusion extraction



14

Fuctional

FailureFail Mode Basic Failure Cause Effects of Failure

1. Traction gear with problem. Damage by fatigue.

2. Conveyor chain with problem. Damage by fatigue.

Conveyor chain locked.

Damage by fatigue.

2. Conveyor chain with

inappropriate length.

Damage by fatigue and corrosion.

3. Pins with problem. Damage by fatigue.

Shovels drag locked.

Damage by fatigue.

Loss of insulation from excessive

moisture



Disarm devices.


Damage by fatigue.



5. Mat of belt with problem. Damage by fatigue.

FF 4.a

FF 2.b

FF 3.a


1. Chain link with problem.Stop Diffuser.

Stop the production

line.

Stop Diffuser.

Stop the production

Stop Diffuser.

Stop the production

line.






Figure 15 – (Part 1) MCC: maintenance plan based on decisions diagram to diffusion extraction

Activity Description Freq

1. Electric motor with problem. PredictiveVibration analysis.

Isolation analysis.

Once a month

Once a year

2. Mat drive with problem. Predictive Thermographic analysis. Once a month

3. Reducer mechanical damaged. Predictive Vibration analysis. Once a month

4. Automation with problem. Preventive Formatting automation computers. Once a year

5. Conveyor chain with problem. Predictive Ultra sonic analysis. Once a quarter

1. Flaps misfits. Preventive Regulation of Flaps. Once a month

2. Shovels drag with problem. PreventiveVisual inspection and replacement of

damaged shovels.Once a quarter

FF 1.c 1. Bagasse input insufficient. InspectionInspect if there are remaining bagasse

and correct operation when necessary.Thrice a day


Isolation analysis.

Once a month

Once a year

2. Diffuser drive with problem. Predictive Thermographic analysis. Once a month

3. Reducer mechanical with

problem.Predictive Vibration analysis. Once a month


5. Diffuser shaft with problem. Predictive Ultra sonic analysis. Once a year

FF 2.a

Maintenance Plan

FF 1.b

Failure Fail Mode Technique

FF 1.a

FF 1.a




15

Figure 15 – (Part 2) MCC: maintenance plan based on decisions diagram to Diffusion Extraction

Activity Description Freq

1. Traction gear with problem. Preventive Restoration of gears. Once a year

2. Conveyor chain with problem. Predictive Ultra sonic analysis. Once a quarter

1. Chain link with problem. Predictive Ultra sonic analysis. Once a year

2. Conveyor chain with

inappropriate length.Inspection

Inspect the chain size and adjust when

necessary.Once a quarter

3. Pins with problem. Predictive Ultra sonic analysis. Once a quarter

4. Shovels drag with problem. PreventiveVisual inspection and replacement of

damaged shovels.Once a quarter


Isolation analysis.

Once a month

Once a year

2. Mat drive with problem. Predictive Thermographic analysis. Once a month

3. Reducer mechanical damaged. Predictive Vibration analysis. Once a month


5. Mat of belt with problem. InspectionInspect the belt and replace necessary

partsOnce a year

FF 2.b

Fail Mode TechniqueMaintenance Plan

FF 4.a

FF 3.a

Failure


3.4. Modeling and Monte Carlo simulation

3.4.1. Time to next failure (TTNF)

The data of TTFN are presented in Figure 4. For data of TTFN, the ExpertFit ® certifies that

the Weibull distribution adequately represent the behavior of 04 subsystems. The data

obtained in the adherence tests are shown in Figure 16. The modeling presented in Figure 16

presents the form of β and η scale values for each subsystem. Notice that the order of values is

less than 1, as bibliographic review, it is concluded that the subsystems have failure rates λ (t)

decreasing.

Figure 16 - Weibull modeling Time to Next Failure



16


3.4.2. Reliability simulation

As observed in Figure 16, the data from the TTNF were well represented by the Weibull

distribution. Through provided parameters by ExpertFit ® (η and β) it was made MC

simulation generating random values for the TTNF of each subsystem. Thus, it estimated the

reliability of the subsystems and the overall system. The simulation was performed with the

help of Microsoft Excel ® software, generating random numbers by Random () function, with

values ranging between 0 and 1, 10,000 simulations had been performed. In Figure 17 and 18

the used formulas and obtained values from simulations are presented.

Figure 17 - Formulation in excel ® for MC simulation times to next failures


Figure 18 - Simulation of time to next failure


For the reliability estimation is necessary to check which simulated value is greater than the

set time. This test, in other words, checks failure before the time at which you want to

calculate the reliability defined in cell time for reliability calculation (h) that, in this case is

one hour. The system only works if there are not failures in the four subsystems. Figures 19

and 20 show the formulas and results where (1) means failure.



17

Figure 17 - Formulation for checking time to next failure


Figure 18 – Checking time to next failure


Finally, it was counted the number of simulations that showed zero failure for the four

subsystems and it was calculated the ratio to the total number of simulations that returned

failures in a subsystem or more. The Excel formulation and the results are presented in

Figure 19. As it can be seen, the reliability of the production line running for an hour without

failure in any of its subsystems is 56.27%. Figure 20 shows the reliability chart for each

subsystem and for the production line in general.

Figure 19 - Reliability calculation formulation



18

Source: the authors (2016).

Figure 20 – System and subsystems reliability curves

Source: the authors (2016).

4. Conclusions

The methodology used in this study showed interesting results. The use of TPM concepts,

with the OEE calculation, section 3.2, which clearly guided the subsystems studied required

more attention, the extraction through diffusion. Thus, efforts were directed at the

development of MCC methodology for this subsystem, section 3.3.

For determining behavior of line production, the MC modeling and simulation has been

developed, section 3.4, showing good results easily applied, making it possible to define the

simulated reliability that according to Figure 20, was 56.27% for one hour of production. The

Reliability presented too low value for a short time, showing great instability in the system.

During the modeling, it was also concluded section 3.4, that the entire system is in the infant

mortality phase, with decreasing failure rate, since all parameters form (β) of the subsystems



19

presented values lower than 1. Generally, during this phase, the nature of breakdowns and

problems (downtime) is coming from design failure and / or installation, and not for the

equipment wearing out or aging. Despite the production line in question has been founded in

2008, classifying it as being in the infant mortality period, makes sense because, in general,

the industrial complex (Sugarcane industry) went through an expansion project in every

production level in the last three years, where every part of industrial automation has been

replaced and rebuilt.

Given these findings, it remains to say that the maintenance plan based on MCC, Figure 15,

should be put into practice only when the system gets mature, i.e, present constant failure rate,

with shape parameter (β) close to 1. While the failure rate is decreasing, smaller form

parameter 1, the company should focus its work on corrective maintenance of their problems,

always seeking to effectively eliminate project errors.

Based on data analysis, section 3.1, the production line presented availability longer than

95%. For continuous production line, this loss of 5% of maintenance downtime, may

implicate irreparable losses. It should be emphasized that, due to the expansion project,

mentioned earlier, it is justified by a 95% availability, because practicing it, after an

expansion of this magnitude, the production line goes through a long period of child maturity,

named "test crop" by sugarcane industry.

References

ANVARI, F; EDWARDS, R.; STARR, A. Evaluation of overall equipment effectiveness based on market.

Journal of Quality in Maintenance Engineering. v. 16, ed. 3, p. 256-270, 2010.

ARAÚJO, E. G. Confiabilidade Aplicada a Sistemas Elétricos Industriais. 2011. 96 f. Dissertação (Mestrado

em Engenharia Elétrica) – Programa de Pós-Graduação em Engenharia Elétrica, Universidade Federal de São

João Del-Rei. São João Del-Rei, MG, 2011.

CHAN, F. T. S.; et. al.. Implementation of total productive maintenance: A case study. International of Journal

Productions Economics, 95., 2003 29 Oct; Elsevier Science Limited, 2005. p. 71-94.

CHENG Z.; JIA X.; GAO P.; WU S.; WANG J. A framework for intelligent reliability centered maintenance

analysis. Reliability Engineering and System Safety. 2008; 93:784–92.

DIEDRICH, A.; SELLITTO, M. A. Manutenção Centrada em Confiabilidade: estudo de caso na indústria de

bebidas. Produção em Foco, v. 4, n. 1, p. 133-155, 2014.

FOGLIATTO, Flávio Sanson; RIBEIRO, José Luiz Duarte. Confiabilidade e Manutenção Industrial. Rio de

Janeiro: Editora Elsevier, 2009.

LAFRAIA, João Ricardo Barusso. Manual de Confiabilidade, Mantenabilidade e Disponibilidade. Rio de

Janeiro: Qualitymark Editora: PETROBRAS, 2014.



20

MARTINS, V. L. M.; WERNER, L.; PINTO, F. T. Uso da simulação de Monte Carlo para avaliação da

confiabilidade de um produto. In: XIII SIMPOI - Simpósio de Administração da Produção, Logística e

Operação. São Paulo, SP, Brasil, 25 a 27 de Agosto de 2010.

MENDES, A. A. Manutenção Centrada em Confiabilidade: Uma abordagem quantitativa. Dissertação de

Mestrado. Programa de Pós-Graduação em Engenharia de Produção, UFRGS. Porto Alegre, 2011.

MEYER, Paul L. Probabilidades: aplicações à estatística. Tradução Ruy de C. B. Lourenço Filho. Editora:

Livros Técnicos e Científicos (LTC). Rio de Janeiro. 2.ª Edição, 1983.

MORAD, A. M.; MOHAMMAD, M. P.; SATTARVAND, J. Application of reliability-centered maintenance for

productivity improvement of open pit mining equipment: Case study of Sungun Copper Mine. Journal Central

South University, p. 2372-2382, 2014.

PEXA, Martin et al. Reliability and risk treatment centered maintenance. Journal of Mechanical Science and

Technology. v. 28, ed. 10, p. 3963-3970, 2014.

PINJALA, S.; PINTELON, L.; VERECKA, A. An empirical investigation on the relationship between business

and maintenance strategies. International Journal of Production Economics, v.104, n.3, p.214-229, 2006.

RAMOS FILHO, J.; ATAMANCZUK, M.; MARÇAL, R. Seleção de técnicas de manutenção para processo de

armazenagem pelo método de análise hierárquica. Produção Online, v.10, n.1, p.142-166, 2010.

RAPOSO, J. Manutenção centrada em confiabilidade aplicada a sistemas elétricos: uma proposta para uso

de análise de risco no diagrama de decisão. Dissertação (Mestrado em Engenharia Elétrica) - Universidade

Federal da Bahia. Salvador, 2004.

RAUSAND, M. Reliability centered maintenance. Reliability Engineering and System Safety. Northern Ireland:

Elsevier Science Limited, 1998. p. 121-132.

SINGH, R. et. al. Total Productive Maintenance (TPM) Implementation in a Machine Shop: A Case Study. In:

Nirma University International Conference on Engineering, 3., 2012. Elsevier: Procedia Engineering, 51, 2013.

p. 592-599.

SELLITTO, M. Formulação estratégica da manutenção industrial com base na confiabilidade dos

equipamentos. Produção, v.15, n. 01, p.044-059, 2005.

SELVIK, J.T.; AVEN, T. A framework for reliability and risk centered maintenance. Reliability Engineering

and System Safety. 96., 2011, p. 324-331.

TAKAHASHI, Yoshikazu; OSADA, Takashi. Manutenção Produtiva Total. Tradução Outras Palavras. São

Paulo: Instituto IMAN, 1993.

RELIABILITY ASSESSMENT IN SUGARCANE INDUSTRY USING … · 2016-11-11 · 2.6. Weibull probability...

Documents

Transcript of RELIABILITY ASSESSMENT IN SUGARCANE INDUSTRY USING … · 2016-11-11 · 2.6. Weibull probability...